Papers by Yiyan Qi
Beyond Function-Level Search: Repository-Aware Dual-Encoder Code Retrieval with Adversarial Verification (2025.findings-emnlp)
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| Challenge: | escalating complexity of modern codebases has intensified the need for code retrieval systems capable of interpreting cross-component change intents. |
| Approach: | RepoAlignBench is a benchmark designed to evaluate repository-level code retrieval . the benchmark proposes an adversarial reflection-augmented dual-tower architecture . |
| Outcome: | The proposed framework achieves 12.2% Top-5 Accuracy and 7.1% Recall improvements over state-of-the-art benchmarks. |
VLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training (2025.findings-emnlp)
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| Challenge: | a significant drawback of Vision-language Models is their reliance on static training data, leading to outdated information and limited contextual awareness. |
| Approach: | They propose a framework with knowledge-enhanced reranking and noise-injected training to improve the VLM's ranking ability. |
| Outcome: | The proposed framework is based on a simple yet effective instruction template and is able to induce its ranking ability and serve it as a reranker to precisely filter the top-k retrieved images. |
ReAttn: Improving Attention-based Re-ranking via Attention Re-weighting (2026.findings-eacl)
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| Challenge: | Attention-based re-ranking methods are highly concentrated a small subset of tokens within a few documents, making others indistinguishable. |
| Approach: | They propose a post-hoc re-weighting strategy that uses attention weights to reduce lexical bias and emphasize distinctive terms. |
| Outcome: | The proposed method reduces lexical bias and emphasizes distinctive terms across documents, while maintaining a balanced distribution across informative tokens. |
MasRouter: Learning to Route LLMs for Multi-Agent Systems (2025.acl-long)
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| Challenge: | Multi-agent systems (MAS) powered by Large Language Models (LLMs) have been demonstrated to push the boundaries of LLM capabilities, yet they often face significant costs and challenges in dynamic LLM selection. |
| Approach: | They propose a multi-agent system routing solution that integrates all components of MAS into a unified routing framework. |
| Outcome: | The proposed solution is high-performing, cost-effective, and efficient . it reduces overhead by up to 52.07 compared to current methods on HumanEval . |
Retrieval, Reasoning, Re-ranking: A Context-Enriched Framework for Knowledge Graph Completion (2025.naacl-long)
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| Challenge: | Existing embedding-based methods rely on triples in the KG, which is vulnerable to specious relation patterns and long-tail entities. |
| Approach: | They propose a context-enriched framework for KGC that uses a large language model to generate potential answers for each query triple. |
| Outcome: | The proposed framework improves on FB15k237 and WN18RR datasets. |
Golden Touchstone: A Comprehensive Bilingual Benchmark for Evaluating Financial Large Language Models (2025.findings-emnlp)
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Xiaojun Wu, Junxi Liu, Huan-Yi Su, Zhouchi Lin, Yiyan Qi, Chengjin Xu, Jiajun Su, Jiajie Zhong, Fuwei Wang, Saizhuo Wang, Fengrui Hua, Jia Li, Jian Guo
| Challenge: | Existing financial benchmarks suffer from limited language and task coverage, low-quality datasets, and inadequate adaptability for LLM evaluation. |
| Approach: | They propose a bilingual benchmark for financial LLMs that assesses models’ language understanding and generation capabilities. |
| Outcome: | The proposed bilingual benchmark assesses models’ language understanding and generation capabilities. |